An Empirical Evaluation Of Performance Of Machine Learning Techniques On Imbalanced Software Quality Data

Ruchika Malhotra & Megha Khanna
The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly....
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